At the end of last week’s article we mentioned that the National Cancer Institute is conducting studies on cancers using a new approach. Rather than studying cancer tumors according to which organs are affected — such as kidney cancer or lung cancer — cancer researchers will divide the patients into different cohorts according to the gene mutations of the disease.
The underlying basis for trying this new approach is that Oncology researchers have long observed that different people respond differently to cancer treatments like chemotherapy and radiation. Why is this? One obvious possible reason is that each of us — as individuals with our own unique genetic traits — are likely to be more or less susceptible to disease, or more or less resistant to treatment protocols. But exposure to different virus pathogens over the course of one’s lifetime might also contribute to the equation.
New All-in-One Virus Detection Test
Certainly viruses have an effect; some are known to cause cancer directly. But testing for all viruses is hard, until now. In the past, doctors had to run tests for exposure to each of the major viruses one at a time. But in a startling discovery last week, researchers announced a breakthrough: a single test that can identify all the virus exposures an individual has had over the course of their entire lifetime. The ability to determine at a comprehensive list of all virus exposures is a significant breakthrough for doctors and researchers trying to understand and treat disease. As this test comes into wider usage, it will increase our ever-growing understanding of the correlation between risk factors for cancer and other mortal diseases.
Healthcare Disease Research is Undergoing a Radical Shift Thanks to Big Data
While it may seem like we have a lot of patient data on file, the potential for collecting more data is enormous. Companies like Apple computer are pushing their Apple Health Kit application programming interface to encourage developers to connect health researchers with data collection from portable electronic devices, like fitness trackers or the Apple Watch. In time, fitness trackers and other yet to be invented personal medical testing devices will feed more patient data into our collective “healthcare database.”
Electronic Health Records Help Determine Medical Best Practices
The transition toward Electronic Health Records (EHR) is providing these researchers with the new treasure trove of statistical data. Want to know which treatment is best for a particular disease? Interested to know if the expensive treatment is actually better than the more economical one? The data is coming in. This is driving the healthcare delivery system toward what’s known as Evidence-Based Medicine, which uses statistical research methods to determine best treatment practices for patients.
There’s a bit more to the story. As Evidence-Based Medicine becomes the norm, the giant ship of healthcare delivery is simultaneously turning away from payment for each individual procedure, a compensation approach commonly known as Fee-for-Service (FFS for short). Instead, more and more healthcare providers will be compensated on the basis of providing managed-care. Many physicians are beginning to participate in programs known as Accountable Care Organizations (ACOs), which provide bonus incentives for healthy patient outcomes.
What Can Medical Researchers Do with Aggregated Healthcare Data?
In the hands of smart researchers, this aggregate patient data can lead to some surprisingly accurate insights. Fundamentally there’s nothing new about this approach; the lifeblood of an insurance company is the actuary, who predicts mortality when issuing policies like life insurance. But this kind of information has not been so readily available to the public as it is today. And over time, there is the tantalizing potential (from the researcher’s point of view at least!) for combining it with personal health tracker information via tools like the Apple Health Kit. What’s possible now?
Well Swedish researchers recently introduced a very short survey which you can take online. By asking a series of simple questions, the program can make a very educated guess about your own mortality. Right now, this program uses statistical modeling to correlate known risk factors — like smoking — with questions about person’s own self-assessment of their fitness to generate an answer to the question: “How Long Will I Live?”. Of course we’re obsessed with youth. Microsoft recently made a huge splash with its age analysis tool, which attempts to correlate an image of your face with how old it thinks you look. But it’s easy to see where this is going. If such assessments also included a person’s individual genetic mutations as well as their risk factors from exposure to viruses and other health issues, the results would be even more accurate.
What Do You Think?
We’re curious what you think. Does aggregated health data collection seem like a good idea if it helps prevent and cure disease? Or is it an invasion of privacy? Would you want to know how long you’ll live? Or would you rather the future reveal itself day by day?
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